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《Infrared Technology》 2014-05
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Multi-sensor Weighted Data Fusion Algorithm Based on Global State Estimation

SI Ying-li;YANG Xin-yu;CHEN Yong;XIANG Jing-bo;GUO Shi-wei;China Airborne Missile Academy;Department of Computer Science and Technology,Xi'an Jiaotong University;  
The principle of least squares based on self-learning weighted data fusion algorithm(PLS-SWFA) has been widely used to fuse measured data in multi-sensor systems. However, via extensive analysis and evaluation, we find that PLS-SWFA has low data fusion accuracy in two-sensors systems and also has low anti-jamming ability. To address this problem, our paper proposes a multi-sensor weighted data fusion algorithm(GSE-MWFA) based on global state estimation. Based on the state estimation of kalman filter, GSE-MWFA can use the historical data to make the fused data fully approximate the real-data of target state. Through extensive theoretical analysis and experiments, our data show that GSE-MWFA achieves higher fusion accuracy and greater anti-jamming ability in comparison with the existing algorithms.
【Fund】: 中国空空导弹研究院科技创新基金 编号:201306S08
【CateGory Index】: TP212;TP202
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